Bachelor 2023/2024

Business Intelligence Technologies

Type: Elective course (Supply Chain Management and Business Analytics)

Area of studies: Management

Delivered by: Department of Business Informatics

Where: Graduate School of Business

When: 4 year, 1 module

Mode of studies: offline

Open to: students of all HSE University campuses

Instructors: Irina Nikiforova / Nikivincze

Language: English

ECTS credits: 4

Contact hours: 40

Course Syllabus

Abstract

The discipline is focused on the study and practical skills of working with modern information technologies used by managers to support decision-making in logistics controlling, management and design of supply chains. The discipline introduces specific methods of data analysis of logistics business processes, data storage and processing, specific tasks and methods of operational and data mining, scenario planning and modeling methods. Practice sessions are met using modern data analysis and analysis systems, solution finders using a low-code approach, and industrial planning model development tools.

Learning Objectives

• The course is aimed at building practical skills of working with modern information technologies used by managers to support decision-making in logistics controlling, management and design of supply chains probability.

Expected Learning Outcomes

• A student understands the use of data analytical tools to support problem solving at various levels of management of the logistics system of the company.
• A student selects and uses data collection and storage tools.
• A student selects and uses tools for building analytical reporting.
• A student analyzes and compares the functionality of Business Intelligence systems.
• A student is able to visualize and to analyze spacial data using specialized software.
• A student chooses methods of analyzing economic data depending on the specifics of the problem being solved.
• A student is able to create and interpret data processing and analytical models using specialized software.
• A student forms the concept of information and analytical support for managerial decision-making in the organization.

Course Contents

• Introduction to Business Analytics
• Data collection and storage technologies
• Analytical reporting and dashboards
• Business Intelligence systems
• Geoinformation systems and tools for visualization and analysis of spatial data
• Data mining and knowledge extraction

Assessment Elements

• Presentation
  A 7-10 minutes presentation on the topic of the course.
• Test
  Electronic test in LMS for 10 minutes.
• Participation
  Solving in-class assignments. Participating discussions.
• Practice
  Group practical assignments.
• Project
  The aim of the project is to develop a prototype analytical solution for a logistics and supply chain management problem using data processing and business intelligence tools covered in the course: Loginom, database management systems, some business intelligence tools. Public defense of the project will take place on the scheduled exam date. All members of the project team must be present at the defense. The format of the exam is face-to-face, as scheduled (except for students who have been officially approved for distance learning mode).

Interim Assessment

• 2023/2024 1st module
  0.1 * Participation + 0.25 * Practice + 0.15 * Presentation + 0.4 * Project + 0.1 * Test

Bibliography

Recommended Core Bibliography

• Greg Deckler. (2019). Learn Power BI : A Beginner’s Guide to Developing Interactive Business Intelligence Solutions Using Microsoft Power BI. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2252653
• Joshua N. Milligan. (2019). Learning Tableau 2019 : Tools for Business Intelligence, Data Prep, and Visual Analytics, 3rd Edition. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2092866
• Kantardzic, M., & Recorded Books, I. (2019). Data Mining : Concepts, Models, Methods, and Algorithms (Vol. Third edition). [Place of publication not identified]: Wiley-IEEE Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2282578
• Murphy, T., & SAS Institute. (2018). Infographics Powered by SAS : Data Visualization Techniques for Business Reporting / C Travis Murphy. Cary, NC: SAS Institute. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1805001
• Nabavi, M., & Olson, D. L. (2019). Introduction to Business Analytics. New York: Business Expert Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1922612
• S. Christian Albright, & Wayne L. Winston. (2019). Business Analytics: Data Analysis & Decision Making, Edition 7. Cengage Learning.

Recommended Additional Bibliography

• Jukic, N., Vrbsky, S., & Nestorov, S. (2017). Database Systems : Introduction to Databases and Data Warehouses. Burlington, Virginia: Prospect Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1562389
• Konecny, G. (2014). Geoinformation : Remote Sensing, Photogrammetry and Geographic Information Systems, Second Edition (Vol. Second edition). Boca Raton, FL: CRC Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1631976
• Барсегян А., Куприянов М., Степаненко В., Холод И. - Технологии анализа данных: Data Mining, Text Mining, Visual Mining, OLAP. 2 изд. - 5-94157-991-8 - Санкт-Петербург: БХВ-Петербург - 2008 - 335156 - https://ibooks.ru/bookshelf/335156/reading - iBOOKS